EmptyDroplets (FDR <= 0.1) + scDblFindersetwd("/media/jacopo/Elements/re_align/MM/PRJNA732205/SAMN19314092/SRR14629352/")
# Load the libraries (from Sarah script + biomart)
library(tidyverse) # packages for data wrangling, visualization etc
library(Seurat) # scRNA-Seq analysis package
library(clustree) # plot of clustering tree
library(ggsignif) # Enrich your 'ggplots' with group-wise comparisons
library(clusterProfiler) #The package implements methods to analyze and visualize functional profiles of gene and gene clusters.
library(org.Hs.eg.db) # Human annotation package neede for clusterProfiler
library(ggrepel) # extra geoms for ggplo2
library(patchwork) #multiplots
library(reticulate)
Load and do the QC for the cellranger data
#list.files(".")
dat <- Read10X(data.dir ="./out/counts_filtered/")
dat <- CreateSeuratObject(dat) # Create the seurat object from the 10x data
kb.initial <- dat@assays[["RNA"]]@counts@Dim[[2]]
cat("Initial number of cells:", kb.initial,
"\nNumber of genes:", dat@assays[["RNA"]]@counts@Dim[[1]])
## Initial number of cells: 13291
## Number of genes: 36601
Empty cells were already filtered, check for % mt RNA and death markers:
# first calculate the mitochondrial percentage for each cell
dat$percent_mt <- PercentageFeatureSet(dat, pattern="^MT.")
# make violin plots
mt_rna = 15
max_counts = 30000
# Check some feature-feature relationships
# % mt RNA vs n Counts, n Features vs n Counts
# Check some feature-feature relationships
# % mt RNA vs n Counts, n Features vs n Counts
VlnPlot(dat, features = c("nCount_RNA", "nFeature_RNA", "percent_mt")) + geom_hline(yintercept=mt_rna, linetype = "dotted")
plot1 <- FeatureScatter(dat, feature1 = "nCount_RNA", feature2 = "percent_mt")
plot1 <- plot1 + geom_hline(yintercept=mt_rna, linetype = "dotted")
plot2 <- FeatureScatter(dat, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot2 <- plot2 + geom_vline(xintercept = max_counts, linetype = "dotted")
plot1
plot2
## cells retained by mt RNA content ( 15 %): 5640
## percentage of retained cells: 42.43 %
## cells retained by counts ( 30000 ): 5637
## percentage of retained cells: 42.41 %
Check the distribution of the cells with low counts and control death markers:
min_counts = 600
hist(dat@meta.data$nCount_RNA, breaks = 100, xlab = "Counts")
hist(dat@meta.data$nCount_RNA, breaks = 1000, xlab = "Counts", xlim = c(0,5000))
hist(dat@meta.data$nCount_RNA, breaks = 10000, xlab = "Counts", xlim = c(0,1000))
abline(v=min_counts, col="red", lty = 3)
The evident peak of cells with < 200 counts could contain dying
cells.
# Subset the dataset to focus only on those cells with low counts
dat.lowcount <- subset(dat, subset = nCount_RNA < min_counts)
# Get the mean of the counts for each gene and sort them decreasing
meanCounts <- rowMeans(GetAssayData(object = dat.lowcount, slot = 'counts'))
meanCounts <- sort(meanCounts, decreasing = T)
# A boxplot can help to observe the distribution of the means
#boxplot(meanCounts)
# Print the most highly expressed genes
head(meanCounts, 30)
## MALAT1 IGLV2-8 MT-CO2 RPS27 EEF1A1 MT-ND3 MT-CO3 MT-CO1
## 13.947578 7.215112 5.062545 3.020607 3.017715 2.901302 2.579176 2.534707
## RPL41 CD74 RPLP1 RPL10 RPL34 RPS12 RPS8 RPL39
## 2.272957 2.145336 2.106291 2.104483 2.071222 2.053145 2.019523 1.949747
## MT-CYB RPL30 MT-ND4L RPL32 RPS23 RPS27A RPL13 RPL11
## 1.926970 1.895879 1.891179 1.820318 1.642805 1.619306 1.591829 1.528200
## RPS28 B2M RPS15A RPL18A RPL28 MT-ATP6
## 1.523138 1.506508 1.433478 1.403471 1.391902 1.296095
## cells retained by counts ( 600 ): 2870
## percentage of retained cells: 21.59 %
dir.create("result")
saveRDS(dat, file = "./result/SAMN19314092_clean_QC.Rds")
#Normalize
dat <- NormalizeData(dat)
# Find the first 4000 variabe features
dat <- FindVariableFeatures(dat, selection.method = "vst", nfeatures = 4000)
Set mean expression to 0 and variance across 1 to avoid highly expressed genes drive the forwarding analyses. Since negative expression is meaningless, scaled data are useful only for UMAP and clustering
# scale data, the scaled data are saved in:
# dat[["RNA"]]@scale.data
all.genes <- rownames(dat)
dat <- ScaleData(dat, vars.to.regress = c("percent_mt","nCount_RNA"))
dat <- RunPCA(dat, features = VariableFeatures(object = dat), verbose = F, seed.use = 1)
print(dat[["pca"]], dims = 1:5, nfeatures = 5)
## PC_ 1
## Positive: IGLV2-8, MZB1, P4HB, SEC11C, ITM2C
## Negative: HLA-DRA, IGKV3-11, HLA-DPA1, EEF1A1, IGHV3-30
## PC_ 2
## Positive: RPL10, RPLP1, EEF1A1, GAPDH, ACTB
## Negative: ITM2C, TXNDC5, CCDC144A, PRPSAP2, IGLC2
## PC_ 3
## Positive: IGLV2-8, PRDX4, CST3, SEC11C, NUCB2
## Negative: NEAT1, HLA-DPA1, RGS10, HLA-DRA, HLA-DPB1
## PC_ 4
## Positive: ID2, ID1, NR4A2, EDN1, DDIT4
## Negative: PSAT1, TNFRSF17, SELPLG, ACTG1, PHB
## PC_ 5
## Positive: HIST1H2BJ, LINC01480, NME2, HIST2H2AA4, CYTOR
## Negative: SELPLG, PSAT1, SCNN1B, SLC1A5, CD200
UMAP is a graph-based method of clustering. The first step in this process is to construct a KNN graph based on the euclidean distance in PCA space:
dat <- FindNeighbors(dat, dims = 1:20)
The graph now can be used as input for the function
runUMAP()
dat <- RunUMAP(dat, dims = 1:20, seed.use = 1)
DimPlot(dat, reduction = 'umap', seed = 1)
## QC metrics
## markers